What this category of data looks like
Steel smelting service inquiry and response data comes from smelting process specifications, equipment operation and maintenance logs, batch quality inspection reports, window inquiry records, and environmental compliance documents. Process specifications and compliance documents are updated annually or quarterly. Operation and maintenance logs and batch quality inspection reports update daily alongside production batches. Inquiry records synchronize daily. Most documents combine structured and semi-structured formats. They include fields such as equipment number, smelting temperature, pressure, and flow rate. Units used include degrees Celsius, megapascals, and cubic meters per hour. The data also includes standardized response scripts and user inquiry classification tags.
Constraints on vector models and indexing workflows
The mixed structure and update schedule of steel smelting service inquiry and response data create multiple constraints for vector models and indexing. First, the data includes short-text inquiry records and long-form process specifications. Systems must adapt chunking rules for varying text lengths, to prevent overly fine chunking that breaks semantic connections or overly coarse chunking that prevents precise matching. Second, daily production batch data and high-frequency inquiry records require indexes to support incremental updates. This ensures retrieval results stay synchronized with the latest production status. Third, multi-unit process parameter fields and standardized response tags require vector models to encode both numerical values and text semantics. Indexes must also retain tag association fields to support classified recall.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapts to the mixed text length of steel smelting process specifications and inquiry records, avoids overly cluttered or overly short single chunks |
| Overlap length | 100–150 characters | Retains semantic connections between chunks, prevents loss of context for process parameters that span multiple chunks |
| Similarity threshold | 0.72–0.85 | Aligns with the semantic similarity range for steel industry terminology, avoids retrieving irrelevant process documents or inquiry records |
| Single search limit | Top 8 results | Balances retrieval scope and context length, adapts to the complexity of professional questions for steel smelting business |
| maxToken | 8000 | Meets the context loading requirements for long-form process specifications, ensures complete transmission of professional parameter information |
| defaultToken | 200 | Used for vector encoding length of standardized response scripts, ensures semantic matching accuracy for short texts |
Three common configuration errors
- Phenomenon: A custom Qdrant vector database fails to connect to the knowledge base normally. The interface shows connection timeout or field mismatch. Cause: The index mapping of the vector database is not configured according to the structured fields of steel smelting data, leading to failure of correct embedding of professional parameters into the vector space.
- Phenomenon: Retrieval results return empty after enabling Tencent Hunyuan vector model, or a prompt indicates invalid channel configuration. Cause: The exclusive interface address and authentication key of the Tencent Hunyuan vector model are not correctly filled in the FastGPT model supplier configuration, and the vector encoding requirements for steel industry professional corpus are not adapted.
- Phenomenon: The number of retrieval results is far lower than expected after the index is enabled, or duplicate recall occurs. Cause: Chunk length settings are unreasonable. Overly long chunks cause excessive semantic overlap, or insufficient overlap length prevents correct association of cross-chunk process parameters.
How to confirm correct configuration
- Upload one steel smelting process specification and one typical inquiry question, run a retrieval test, check the semantic matching degree between the retrieved results and the question, and adjust the similarity threshold to a range that meets business requirements.
- View the vector database index logs, confirm that the incremental update task has synchronized production data according to the preset schedule, with no delayed or failed records.
- On the model supplier configuration page, verify the interface connectivity of the vector model, ensuring that vector encoding for steel professional corpus can be generated normally.
- Test the chunked text fragments, confirm that the chunk length and overlap length settings retain the complete semantic connection of process parameters.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.